Creare modelli di previsione sensibili

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Questo esercizio fa parte del corso

Previsioni finanziarie in Python

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In this chapter, we will learn the basics of financial statements, with a specific focus on the income statement, which provides details on our sales, costs, and profits. We will learn how to calculate profitability metrics and finish off what we have learned by building our profit forecast for Tesla!

Exercise 1: Introduction to financial statements Exercise 2: Calculating gross profit Exercise 3: Calculating net profit Exercise 4: Elements within net profit & gross profit Exercise 5: Calculating sales & Cost of Goods Sold (COGS)Exercise 6: Calculating sales Exercise 7: Forecasting sales with a discount Exercise 8: Calculating COGS Exercise 9: Calculating the break-even point Exercise 10: Working with raw forecast datasets Exercise 11: Tesla income statement Exercise 12: Forecasting profit for Tesla

In this chapter, we will learn a bit more about the balance sheet, covering assets and liabilities and specific ratios to help evaluate the financial health and efficiency of a company, as well as how these ratios can assist us in building a great forecast.

Exercise 1: Introduction to the balance sheet Exercise 2: Calculating accounts receivable (debtors)Exercise 3: Bad debts Exercise 4: Calculating accounts payable (creditors)Exercise 5: Understanding accounts payable and receivable Exercise 6: Balance sheet efficiency ratios - Part 1 Exercise 7: Debtor days ratio Exercise 8: Days payable outstanding Exercise 9: Balance sheet efficiency ratios - Part 2 Exercise 10: Days in inventory and asset turnover ratio Exercise 11: Understanding ratios Exercise 12: Calculating balance sheet ratios for Ford Exercise 13: Forecasting the balance sheet for Ford

We have gotten a basic understanding of income statements and balance sheets. However, consolidating data for forecasting is complex, so in this chapter, we will look at some basic tools to help solve some of the complexities specifically relating to finance - working with dates and different financial periods, and formatting our raw data into the correct format for financial forecasting.

Exercise 1: Financial periods and how to work with them Exercise 2: Converting quarters into months Exercise 3: Merging months into quarters Exercise 4: The datetime library Exercise 5: Working with the datetime library Exercise 6: Converting date formats - simple Exercise 7: Converting date formats - explicit Exercise 8: Tips and tricks when working with datasets Exercise 9: Working with datasets - month totals Exercise 10: Working with datasets - combining datasets Exercise 11: Working with datasets - exporting data

In this chapter, we will be exploring two more aspects to creating a good forecast. First, we will look at assumptions, what drives them and what happens when an assumption changes? Next, we will look at variances, as a forecast is built at one point in time, but what happens when the actual results do not correspond to our forecast? We need to build a sensitive forecast that can be sensitive to changes in both assumptions and take into account variances, and this is what we will explore in this chapter.

Exercise 1: Creare modelli di previsione sensibili

Esercizio in corso

Exercise 2: Probabilità ponderata Exercise 3: Sentiment di mercato Exercise 4: Dipendenze e sensitività Exercise 5: Assegnare le dipendenze per vendite e COGS Exercise 6: Costruire un'analisi di sensitività per il margine lordo Exercise 7: Assegnare dipendenze per le spese Exercise 8: Crea un'analisi di sensitività per l'utile netto Exercise 9: Lavorare con le varianze nel forecast Exercise 10: Creare un forecast alternativo Exercise 11: Costruire una gap analysis tra previsioni Exercise 12: Impostare le dipendenze per Netflix Exercise 13: Calcolare una previsione alternativa per Netflix